A generic library of problem solving methods for scheduling applications

Rajpathak, Dnyanesh; Motta, Enrico; Zdráhal, Zdeněk; Roy, Rajkumar

doi:10.1109/tkde.2006.85

Cited by 29 publications

(11 citation statements)

References 53 publications

(70 reference statements)

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“…Moreover, TR subscribes to Allen's [22] representation of standard time and relations. Has-Earliest-Start-Time, Has-Latest-Start-Time, Has-Earliest-End-Time, and HasLatest-End-Time as specified in [9] are typical temporal specification for TR's knowledge modeling.…”

Section: A Knowledge Modeling Framework Potmementioning

confidence: 99%

“…The first stage mainly concentrates on preliminary knowledge discovery with qualitative problem cognition [3][5] [7] [8]. The second stage mainly focuses on verifying the problem specification derived from the first stage, through concrete parameter computing activities [6] [9][10] [11]. In practice, a complex problem and its collaboration solving are often initiated by incomplete, ill structured and poor quality information.…”

Section: Introductionmentioning

confidence: 99%

“…The second temporal relation is also initiated by a cross-domain knowledge dependency that function A demands a parameter b produced by function B, otherwise it could not be triggered. Furthermore, as mentioned in [9], "scheduling deals with the assignment of jobs and activities to resources and time ranges in accordance with relevant constraints and requirements." Here, knowledge dependency relations among task executions are typical "relevant constraints and requirements" in scheduling application.…”

Section: Introductionmentioning

confidence: 99%

“…Therefore, to incorporate temporal knowledge into a knowledge modeling framework is helpful for enhancing crossdomain knowledge coordination among task executions. In this regard, current knowledge modeling techniques as presented in [16][17] [18][19] [20] mainly aims at set up a structured organization for articulating problem-solving components at knowledge-level, rather than scheduling application of knowledge-based collaboration [9].…”

Section: Introductionmentioning

confidence: 99%

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A Novel Knowledge Modeling Framework POTMe and Its Application for Col-laborative Problem Solving

Jiang¹

2016

Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering

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Abstract. Collaborative efforts are inevitably demanded when solving a complex problem, which is often plagued with incomplete, ill-structured, and poor quality information. Therefore, a global knowledge modeling framework is required for cross-domain knowledge coordination. However, current knowledge modeling techniques pay little attention to temporal knowledge that is indispensable for navigating cross-domain collaborative task executions and their collaboration. In view of this challenge, a novel knowledge modeling framework, named POTMe, is investigated in this paper. This knowledge modeling framework provides a structured application context by articulating knowledge dependencies and temporal dependencies among problemsolving activities. The main contribution of this paper is twofold. First, this novel knowledge modeling framework provides a structured problem solving context. It enhances the consistency of global knowledge coordination among collaborative problem-solving activities. Second, temporal knowledge modeling is incorporated into this knowledge modeling framework. It is reified by a temporal reasoning rule, and aims at evaluating cross-domain task collaboration in an incorporated executive environment. [3] believed that a human-engineered activity could be characterized by three traditional engineering activities, i.e., 1) stakeholders define their needs and requirements, 2) engineers design the product based on stakeholders' definitions, and 3) producers build the product based on engineers' designs. These activities are often accompanied by cross-domain knowledge sharing and collaboration. The first and second activities could be characterized by product conceiving behaviors. The third activity could be featured by product producing behavior based on the product blueprint. IntroductionIn collaborative problem solving, qualitative problem-defining process equal to a group of product conceiving behaviors, while quantitative parameter computing and verifying process could be treated as a group of product producing behaviors. Generally, they are two different application stages. The first stage mainly concentrates on preliminary knowledge discovery with qualitative problem cognition . In practice, a complex problem and its collaboration solving are often initiated by incomplete, ill structured and poor quality information. It is always a challenging endeavor

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Section: A Knowledge Modeling Framework Potmementioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

A Novel Knowledge Modeling Framework POTMe and Its Application for Col-laborative Problem Solving

Jiang¹

2016

Proceedings of the 2015 4th National Conference on Electrical, Electronics and Computer Engineering

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“…A large amount of work in knowledge-based systems in the past three decades, like [11], [4], and [27], has concentrated on providing frameworks and tools that support the collaboration of KEs and SMEs with the goal of alleviating the knowledge acquisition bottleneck. However, despite progress shown by existing knowledge acquisition tools like KRAKEN [25], SHAKEN [3] and, more recently, AURA [10] and the Halo extension of the Semantic MediaWiki [18], it is still a complex problem to enable SMEs to capture the knowledge from a domain by themselves, especially for some knowledge types.…”

Section: Introductionmentioning

confidence: 99%

A Formalism and Method for Representing and Reasoning with Process Models Authored by Subject Matter Experts

Gómez-Pérez¹,

Erdmann²,

Greaves

et al. 2013

IEEE Trans. Knowl. Data Eng.

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Abstract-Enabling Subject Matter Experts (SMEs) to formulate knowledge without the intervention of Knowledge Engineers (KEs) requires providing SMEs with methods and tools that abstract the underlying knowledge representation and allow them to focus on modeling activities. Bridging the gap between SME-authored models and their representation is challenging, especially in the case of complex knowledge types like processes, where aspects like frame management, data, and control flow need to be addressed. In this paper, we describe how SME-authored process models can be provided with an operational semantics and grounded in a knowledge representation language like F-logic to support process-related reasoning. The main results of this work include a formalism for process representation and a mechanism for automatically translating process diagrams into executable code following such formalism. From all the process models authored by SMEs during evaluation 82 percent were well formed, all of which executed correctly. Additionally, the two optimizations applied to the code generation mechanism produced a performance improvement at reasoning time of 25 and 30 percent with respect to the base case, respectively. 1INTRODUCTIONB UILDING knowledge-based systems is an activity that has been traditionally carried out by a combination of software and knowledge engineers (KEs) and of subject matter experts (SMEs), also known as domain experts. Software engineers (SEs) are focused on architectural and user interface issues related to the development of software. KEs are focused on knowledge acquisition and representation tasks, with the aim of building the required knowledge bases. For these tasks, KEs usually work in collaboration with SMEs, who normally act as repositories of domain knowledge. The combination of KEs and SMEs, although feasible, has two main drawbacks, first characterized as the knowledge acquisition bottleneck [13]: 1) it is costly and 2) it can be error prone, especially in complex domains.A large amount of work in knowledge-based systems in the past three decades, like [11] Among the different types of knowledge that can be used in knowledge-based systems, e.g., factual, rule or causal knowledge, we focus on process knowledge, which is widely used across domains while posing important challenges for knowledge acquisition. A process can be defined as a naturally occurring or designed sequence of changes of properties of a system or object. Examples of processes include the replication of DNA, the mitosis of the cell or a combustion reaction. Processes encapsulate such things as preconditions and postconditions, results, contents, actors or causes and relate to the sequence of operations and involved events taking up time, space, expertise or other resources, which lead to the production of an outcome. For example, consider the case of a complex chemical reaction comprising several steps, with different inputs and outputs, where it is necessary to reason about what would happen at a certain stage if a previou...

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